Relevant bibliographies by topics / Security of machine learning classifiers

Contents

  1. Journal articles

Academic literature on the topic 'Security of machine learning classifiers'

Author: Grafiati
Published: 14 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Security of machine learning classifiers.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Security of machine learning classifiers"

1

Atnafu, Surafel Mehari, and Prof (Dr ). Anuja Kumar Acharya. "Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (2021): 22–28. http://dx.doi.org/10.35940/ijainn.b1025.041221.

Full text
Abstract:
In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifiers are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.
APA, Harvard, Vancouver, ISO, and other styles
2

Atnafu, Surafel Mehari, and Prof (Dr ). Anuja Kumar Acharya. "Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (2021): 22–28. http://dx.doi.org/10.54105/ijainn.b1025.041221.

Full text
Abstract:
In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifiers are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.
APA, Harvard, Vancouver, ISO, and other styles
3

ALGorain, Fahad T., and John A. Clark. "Covering Arrays ML HPO for Static Malware Detection." Eng 4, no. 1 (2023): 543–54. http://dx.doi.org/10.3390/eng4010032.

Full text
Abstract:
Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) is a novel approach that can enhance machine learning classifier accuracy. The tuning of machine learning (ML) classifiers to increase classification accuracy is needed nowadays, especially with newly evolving malware. Four machine learning techniques were tuned using cAgen, a tool for generating covering arrays. The results show that cAgen is an efficient approach to achieve the optimal parameter choices for ML techniques. Moreover, the covering array shows a significant promise, especially cAgen with regard to the ML hyper-parameter optimisation community, malware detectors community and overall security testing. This research will aid in adding better classifiers for static PE malware detection.
APA, Harvard, Vancouver, ISO, and other styles
4

Katzir, Ziv, and Yuval Elovici. "Quantifying the resilience of machine learning classifiers used for cyber security." Expert Systems with Applications 92 (February 2018): 419–29. http://dx.doi.org/10.1016/j.eswa.2017.09.053.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Gongada, Sandhya Rani, Muktevi Chakravarthy, and Bhukya Mangu. "Power system contingency classification using machine learning technique." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3091–98. http://dx.doi.org/10.11591/eei.v11i6.4031.

Full text
Abstract:
One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.
APA, Harvard, Vancouver, ISO, and other styles
6

Mehanović, Dželila, and Jasmin Kevrić. "Phishing Website Detection Using Machine Learning Classifiers Optimized by Feature Selection." Traitement du Signal 37, no. 4 (2020): 563–69. http://dx.doi.org/10.18280/ts.370403.

Full text
Abstract:
Security is one of the most actual topics in the online world. Lists of security threats are constantly updated. One of those threats are phishing websites. In this work, we address the problem of phishing websites classification. Three classifiers were used: K-Nearest Neighbor, Decision Tree and Random Forest with the feature selection methods from Weka. Achieved accuracy was 100% and number of features was decreased to seven. Moreover, when we decreased the number of features, we decreased time to build models too. Time for Random Forest was decreased from the initial 2.88s and 3.05s for percentage split and 10-fold cross validation to 0.02s and 0.16s respectively.
APA, Harvard, Vancouver, ISO, and other styles
7

Deshmukh, Miss Maithili, and Dr M. A. Pund. "Implementation Paper on Network Data Verification Using Machine Learning Classifiers Based on Reduced Feature Dimensions." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2921–24. http://dx.doi.org/10.22214/ijraset.2022.41938.

Full text
Abstract:
Abstract: With the rapid development of network-based applications, new risks arise and extra security mechanisms require additional attention to enhance speed and accuracy. Although many new security tools are developed, the rapid rise of malicious activity may be a major problem and therefore the ever-evolving attacks pose serious threats to network security. Network administrators rely heavily on intrusion detection systems to detect such network intrusion activity. a serious approach is machine learning methods for intrusion detection, where we learn models from data to differentiate between abnormal and normal traffic. Although machine learning methods are often used, there are some drawbacks to deep analysis of machine learning algorithms in terms of intrusion detection. during this work, we present a comprehensive analysis of some existing machine learning classifiers within the context of known intrusions into network traffic. Specifically, we analyze classification along different dimensions, that is, feature selection, sensitivity to hyper-parameter selection, and sophistication imbalance problems involved in intrusion detection. We evaluate several classifications using the NSL-KDD dataset and summarize their effectiveness using detailed experimental evaluation. Keywords: IDS, Machine Learning, Classification Algorithms, NSL-KDD Dataset, Network Intrusion Detection, Data Mining, Feature Selection, WEKA, Hyperparameters, Hyperparameter Optimization.
APA, Harvard, Vancouver, ISO, and other styles
8

Runwal, Akshat. "Anomaly based Intrusion Detection System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 255–60. http://dx.doi.org/10.22214/ijraset.2021.37955.

Full text
Abstract:
Abstract: Attacks on the computer infrastructures are becoming an increasingly serious issue. The problem is ubiquitous and we need a reliable system to prevent it. An anomaly detection-based network intrusion detection system is vital to any security framework within a computer network. The existing Intrusion detection system have a high detection rate but they also have mendacious alert rates. With the use of Machine Learning, we can implement an efficient and reliable model for Intrusion detection and stop some of the hazardous attacks in the network. This paper focuses on detailed study on NSL- KDD dataset after extracting some of the relevant records and then several experiments have been performed and evaluated to assess various machine learning classifiers based on dataset. The implemented experiments demonstrated that the Random forest classifier has achieved the highest average accuracy and has outperformed the other models in various evaluations. Keywords: Intrusion Detection System, Anomaly Detection, Machine Learning, Random Forest, Network Security
APA, Harvard, Vancouver, ISO, and other styles
9

Abdulrezzak, Sarah, and Firas Sabir. "An Empirical Investigation on Snort NIDS versus Supervised Machine Learning Classifiers." Journal of Engineering 29, no. 2 (2023): 164–78. http://dx.doi.org/10.31026/j.eng.2023.02.11.

Full text
Abstract:
With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade the detection rates of current NIDSs, thorough analyses are essential to identify where ML predictors outperform them. The first step is to provide assessment of most used NIDS worldwide, Snort, and comparing its performance with ML classifiers. This paper provides an empirical study to evaluate performance of Snort and four supervised ML classifiers, KNN, Decision Tree, Bayesian net and Naïve Bays against network attacks, probing, Brute force and DoS. By measuring Snort metric, True Alarm Rate, F-measure, Precision and Accuracy and compares them with the same metrics conducted from applying ML algorithms using Weka tool. ML classifiers show an elevated performance with over 99% correctly classified instances for most algorithms, While Snort intrusion detection system shows a degraded classification of about 25% correctly classified instances, hence identifying Snort weaknesses towards certain attack types and giving leads on how to overcome those weaknesses. es.
APA, Harvard, Vancouver, ISO, and other styles
10

Singh, Ravi, and Virender Ranga. "Performance Evaluation of Machine Learning Classifiers on Internet of Things Security Dataset." International Journal of Control and Automation 11, no. 5 (2018): 11–24. http://dx.doi.org/10.14257/ijca.2018.11.5.02.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources
You might also be interested in the extended bibliographies on the topic 'Security of machine learning classifiers' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography